An exploration of factors influencing self-efficacy in online learning- A systematic review_2018

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review An Exploration of Factors Influencing Self-Efficacy in Online Learning: A Systematic Review https://doi.org/10.3991/ijet.v13i09.8351 Chattavut Peechapol, Jaitip Na-Songkhla!!\", Siridej Sujiva, Arthorn Luangsodsai Chulalongkorn University, Bangkok, Thailand [email protected] Abstract—This review examines 12 years of research by focusing on the following question: What are the factors that influence self-efficacy in an online learning environment? There has been a plethora of research concerning self-efficacy. However, few works have focused on the sources of self-efficacy in online-learning environments. Systematic searches of numerous online data- bases published between 2005 and 2017, which covered factors influencing self-efficacy in online learning context, resulted in the investigation of 25 studies. The data were extracted, organized and analyzed using a narrative synthesis. Results revealed that various factors improved self-efficacy and provided evidence of significant sources of self-efficacy in the context of online learning. Moreover, the investigation provides guidance for further research in designing online learning environments to enhance the self-efficacy of learners. Keywords—Self-efficacy, Online learning, Narrative synthesis, Sources of self-efficacy, Systematic review 1 Introduction Technological advances and easier access to the Internet have led to an increase in online learning compared with traditional learning environments. Online learning offers learning experiences with technology, which provides accessibility, connectivity, flexibility, and ability to promote interactions among learners. As the number of online-learning users continues to increase, there is a need to understand how students can best apply learning strategies to achieve academic success within the online environment. Self-efficacy is the belief in one’s capabilities to organize and execute the requisite actions required to produce particular results [1]. Beliefs about self-efficacy determine level of motivation as reflected in the amount of effort exerted in an endeavor and the length of time devoted to a challenging situation [2]. If persons have a low level of self-efficacy toward a task, they are less likely to exert effort and accomplish the task. Research findings have demonstrated that self-efficacy is a better predictor of academic achievement than other cognitive or affective processes [3]. Therefore, self- efficacy is critical to learning and performance [4]. Student self-efficacy seems particularly important in challenging learning environments, such as an online learn- 64 http://www.i-jet.org

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review ing one where students lack opportunities to interact with others and as a result can become socially isolated [5] , [6]. Also, the drop-out rate among students in online learning environments is higher than that in traditional learning environments [7]. Drop-out rate also related with a lack of self-efficacy [8]. Understanding self-efficacy in online learning is critical to improving online education, which can be a key component of academic success in distance education [4]. However, the focuses of the previous studies were mostly on the situation of self-efficacy in online learning. There have been very few works analyzing factors effecting self-efficacy. As a conse- quence, the objective of the current review is to examine systematically factors that contribute to self-efficacy in the online learning environment, and which have not previously appeared in open literature. 2 Systematic review method A systematic review was based on PRISMA guidelines [9]. These structures are the guidelines on the systematic review to compare all the data that matches preset criteria to answer specific research questions: What are the factors that influence self- efficacy in an online-learning environment? 2.1 Search strategy An extensive search strategy of the ERIC, Scopus, and Web of Science online databases was conducted and separated into two key search terms. The strategy search terms are shown in Fig. 1. Databases: Scopus (876 articles), Web of Science (526 articles) and ERIC (772 articles) Total: 2174 articles Search terms 1 Search terms 2 Factor* OR Influence* OR Effect* OR Correlat* OR Predict* OR Relat* Affect* OR Role* OR Effect* AND AND “Self-efficacy” “Self-efficacy” AND AND “Online learning” OR “e-learning” OR “Online learning” OR “e-learning” OR “Distance learning” OR “Mobile learn- “Distance learning” OR “Mobile learn- ing” OR “Web-based learning” ing” OR “Web-based learning” 25 articles selected Fig. 1. Example of a full-search strategy iJET ‒ Vol. 13, No. 9, 2018 65

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review 2.2 Inclusion and Exclusion criteria Papers for inclusion in the review were limited to publication in the English language between 2005 and 2017. The final search was conducted in September 2017. The study collected only the research concerned with factors that effect self-efficacy in online learning. One reviewer screened titles and abstracts of studies for first selection. After that, all reviewers examined the remaining full texts of studies to determine eligibility for inclusion in the review. Disagreements between reviewers were resolved through discussion of the degree to which articles met exclusion criteria. 2.3 Search outcomes A total of 25 studies were identified for inclusion in the review. Data from the search strategy of online databases yielded 2174 results. After the removal of dupli- cates, the remaining 1513 records were assessed based on titles and abstracts. Moreover, 1090 records were excluded from reviewing the titles and abstracts because these studies did not meet inclusion criteria. The full texts of the remaining 69 studies were examined, and 25 were considered relevant. The process used to reduce and evaluate the records is illustrated in the PRISMA flow diagram as displayed in Fig. 2. Fig. 2. Flow diagram of studies included in review 66 http://www.i-jet.org

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review 2.4 Data Extraction and Synthesis A data extraction table was developed to enable collection of information relevant to the review. All data were collated and manually synthesized. Information extracted from each included a study of sample characteristics (sample size, mean age, gender, and researched location), study design, factor measures, self-efficacy measures, and relevant findings. In addition, a narrative summary was provided. 3 Results and Discussion The results of the review are presented to explore factors influencing self-efficacy in online learning. Summary of included studies within the systematic review can be seen in Table 1. 3.1 Study characteristics The selected studies encompass research investigated between 2005 and 2010 (eight studies), between 2011 and 2015 (eight studies), and between 2016 and Sep- tember 2017 (nine studies). The selected-studies research included 22 survey studies and three quasi-experiments. Table 1. Summary of included studies within the systematic review Authors Sample Charac- Study design Finding (Year) teristics Jashapara and N: 107 Research: Survey - Computer experience had a Tai [10] Independent varia- significant effect on e-learning ble(s): specific self-efficacy (! = .51, p Bates and N: 288 - Personal innovative- < .001). Khasawneh Country: US. ness with IT [11] - Personal innovativeness with IT [21] Gender: - Computer playfulness had a significant influence on e- - Male: (28%) [12] learning specific self-efficacy (! - Female: (72%) - Computer experience = 0.46, p < .001). [13] , [14] , [15] - Computer playfulness had a Mediator variable(s): significant effect on e-learning - E-learning system self- specific self-efficacy (! = 0.40, p efficacy [16] < .001). - Computer anxiety [17] - E-learning specific self-efficacy , [18] mediated the effect of computer Dependent variable(s): experience on perceived ease of use Perceived ease of use of - E-learning specific self-efficacy e-learning partially mediated the effect of systems [19] , [20] personal innovativeness and computer playfulness on per- Research: Survey ceived ease of use. Independent varia- ble(s): -Previous success with online -Previous success with learning technology (r = 0.48, p online learning technol- \" 0.01), Fixed ability (r = -0.32, p \" .01), Acquired skill (r = 0.38, p \" .01), and online learning iJET ‒ Vol. 13, No. 9, 2018 67

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review Authors Sample Charac- Study design Finding (Year) teristics ogy system anxiety (r = -.56, p \" .01) Choi, et al. Age: -Pre-course training [24] - Lower-21: (27%) -Instructor feedback correlated with self-efficacy. - 21-29: - Fixed ability (57%) - Acquired ability - Previous success had a -29-Upper: (16%) -Online learning system anxiety significant effect on self-efficacy N: 223 Mediator variable(s): Online learning self- (! = .2, p \" .05). efficacy [22] , [23] Dependent variable(s): -Instructor feedback was consist- - Outcome expectations - Skill mastery ently significant with self- - Number of hours spent per week efficacy (! = -.11, p \" .05). Research: Survey -Acquired skill had a significant Independent varia- ble(s): effect on self-efficacy (! = .15, p Learner interface, interaction, instructor \" .05). attitude towards stu- dents, instructor tech- -Anxiety had a significant effect nical competence and content [25] , [26] on self-efficacy (! = -.36, p \" .05). -Online learning self-efficacy mediates the relationship be- tween the independent variables and each of the outcomes. - The effect of flow experience on technology self-efficacy in ERP system usage was supported at 99% (path coefficient = 0.296, t = 4.123). - Attitude towards e- learning had a significant effect on technology self-efficacy in ERP system (path coefficient = 0.323, t = 3.864). Wang and Wu N: 76 Mediator variable(s): Receiving elaborate feedback [30] Country: - Attitude towards e- was significantly related to the Taiwan learning [27] difference between students’ - Flow experience [28] self-efficacy on original assign- Chu [32] N: 290 Dependent variable(s): ment and self-efficacy on revised Country: Technology self- assignment (! = .287, p < .05). Taiwan efficacy in ERP system Gender: usage [29] - Age (r = .20, p < .05), tangible - Male: 112 (39%) family support - Female: 178 (61%) Design: quasi- (r =.30, p < .01), and emotional Age: experiment family support (r =.39, p < .01) - 50–64: 215 Independent varia- significantly correlated with (74.14%) ble(s): general internet self-efficacy. - Receiving elaborate - Tangible (r =.22, p < .01) and feedback emotional family support (r =.36, Dependent variable(s): p < .05) - Self-efficacy [31] - Academic perfor- mance Research: Survey Independent varia- ble(s): - Tangible family support - Emotional family support Mediator variable(s): Internet self-efficacy 68 http://www.i-jet.org

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review Authors Sample Charac- Study design Finding (Year) teristics [33] significantly correlated with - 64-Upper: 75 Dependent variable(s): communication internet self- (25.86%) The effects of e-learning efficacy. Mean: 58.59, SD = (Perceived learning, - Emotional family support 5.78 Intent-to-persist in e- contributed significantly to the learning, and learning prediction of general internet satisfaction [34] self-efficacy (! = .38, p < .01) and communication internet self- efficacy (! = .20, p < .01). Chu and Chu N: 317 Research: Survey - Tangible family support con- [35] Country: Independent varia- tributed significantly to the Taiwan ble(s): prediction of general internet Law, Lee and Gender: - Peer support [36] self-efficacy (! = .17, p < .01) Yu [39] - Male: 111 (35.04%) Mediator variable(s): and communication internet self- - Female: 206 Internet self-efficacy efficacy (! = .16, p < .01). Tseng and (64.96%) [33] Kuo [40] Age: Moderator(s): - Age (r = -.32, p < .01), Peer Mean: 54.59 - Aggregate collectiv- support (r = -.38, p < .01), Col- ism [37] lectivism (r = 0.20, p < .01), and N: 365 - Aggregate group Group potency (r = .22, p < .01) Country: Hong potency were significantly associated Kong [37] , [38] with internet self-efficacy. Gender: Dependent variable(s): - Collectivism (r = 0.20, p < .01) - Male: 254 (69.6%) The e-learning outcome: and - Female: 111 perceived learning, (30.4%) persistence and satisfac- Group potency (r = .22, p < .01) tion [34] were significantly associated N: - with internet self-efficacy. Country: Design: Survey - The effect of peer support on Taiwan Independent varia- internet self-efficacy was signifi- ble(s): cant (# = .36, p < .01). - Individual attitude and - Collectivism significantly Expectation moderated the cross-level inter- - Reward and recogni- action between peer support and tion internet self-efficacy (# = .27, p < - Punishment .01). - Challenging goals - Social pressure and - Individual attitude and expecta- competition tion (r = .57, p < .01), Challeng- Dependent variable(s): ing goals (r = .66, p < .01), Clear Self-efficacy direction (r = .52, p < .01), Reward and recognition (r = .52, Design: Survey p < .01), Punishment (r = .42, p < Independent varia- .01), and Social pressure and ble(s): competition (r = .57, p < .01) correlated with self-efficacy. - Individual attitude and expecta- tion (! = 0.122, p < .01), chal- lenging goals (! = 0.429, p < .01), and social pressure and competition (! = 0.262, p < .01) had a significant effect on self- efficacy. - Community identity had a significant positive effect on knowledge-sharing self-efficacy iJET ‒ Vol. 13, No. 9, 2018 69

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review Authors Sample Charac- Study design Finding (Year) teristics - Community identity ( != 0.37, p < 0.05) Jashapara and N: 403 [41] , [42] -Effect of interpersonal trust (! = Tai [54] Country: - Interpersonal trust [43] 0.30, p < 0.01) on knowledge- Gender: , [44] , [45] sharing self-efficacy was signifi- Zang, et al. - Male: Mediator variable(s): cant. [56] 204 (50.6%) - Social awareness [46] , - Female: [47] - Community identity and inter- Shen, et al. 199 (49.4%) - Knowledge-sharing personal trust influenced [61] Age: self-efficacy [48] , [49] , knowledge-sharing behavior Mean: 23 [50] , [51] through the mediation of Dependent variable(s): knowledge sharing self-efficacy. N: 144 Knowledge-sharing Country: Hong behavior [52] , [53] - Personal innovativeness with IT Kong Design: Survey showed significant effect on e- Gender: Factor(s): learning system self-efficacy (! = - Male: 114 (79.2%) - Personal innovative- 0.34, p < 0.001). - Female: 30 (20.8%) ness with IT [11] - Computer playfulness (! = Age: - Computer playfulness 0.18, p < 0.001) had a significant - 25 -: 17 (11.8%) [12] positive effect on e-learning - 25-32: 94 (65.3%) Computer experience system self-efficacy. - 33-40: 25 (17.3%) [13] , [14] , [15] - Computer experience (! =0.39, - 40-Upper: 8 (5.6%) Mediator: p < 0.001) had a significant - Computer Anxiety effect on e-learning system self- N: 406 [18] , [55] efficacy. Country: U.S. - E-learning system self- - E-learning system self-efficacy Gender: efficacy [16] completely mediated the effects - Male: 104 (25.16%) of Dependent variable(s): computer experience and com- Perceived Ease of Use puter playfulness on perceived [20] ease of use - E-learning system self-efficacy Design: Survey partially mediated the effect of Independent varia- personal innovativeness with IT ble(s): on perceived ease of use. Environmental fac- - Perceived responsiveness was tors: observed to influence self- - Perceived responsive- efficacy significantly (path ness [44] coefficient = 0.20, p < 0.01) - Psychological safety - The results revealed the posi- communication climate tive influence of the psychologi- [57] cal-safety communication cli- mate on self-efficacy (path Person factor: coefficient = 0.30, p < 0.01) - Self-efficacy [58] - Self-efficacy partially mediated Dependent variable(s): the relationship between the - Satisfaction [59] psychological-safety communi- - Intention to continue cation climate and the intention participation [59] , [60] to continue participation (Sobel Design: Survey statistics = 2.07, p = .038). Independent varia- ble(s): The number of online courses Number of Online was a significant predictor of self-efficacy to complete an online course (t = 3.48, p < .01) 70 http://www.i-jet.org

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review Authors Sample Charac- Study design Finding (Year) teristics Courses - Motivation directly influenced Wang, et al. - Female: 301 Dependent variable(s): the levels of technology self- [62] (74.14%) - Dimensions of online efficacy (! < .796, p < .001). - No response: 1 learning self-efficacy - Motivation was the mediator Chiu and Tsai (0.7%) - Online learning satis- between [66] faction the learning strategies and tech- N: 256 Design: Survey nology self-efficacy Tang, et al. Country: US. Independent varia- [69] Gender: ble(s): - Social factor correlated with - Male: 121 (47.3%) - Gender basic internet self-efficacy (r = - Education level .29, p < .001) and advanced - Female: 135 internet self-efficacy (r = .37, p < (53.1%) - Previous experience .001) Mediator variable(s): - The social factor had positive N: 244 - Motivation and learn- effects on basic internet self- (All female) ing strategies (Modified efficacy (# = .37, p < .001) and Country:Taiwan motivation strategies for advanced internet self-efficacy (# Age: learning questionnaire = .29, p < .001) - 21-30: 119 (48.8%) [63] - Social factor played an indirect - 31-40: 76 (31.1%) - Online technology role in nurses' intention to use - 41-50: 40 (16.4%) self-efficacy [64] web-based continued learning Dependent variable(s): through basic internet self- N: 318 - Achievement efficacy. Country: - Students’ overall - Confirmation was significantly Chinese satisfaction with the related to perceived self-efficacy Gender: online courses [65] (! = 0.819, t = 15.588) - Male: 130 (40.9%) Design: Survey Independent varia- - Female: 188 ble(s): (59.1%) - Social Factor: manage- Age: rial support, job support - 23: 151 (47.5%) and organizational - 23–30: 145 (45.6%) support [67] - 31–40: 15 (4.70%) - Personal factor: Inter- - 41–50: 6 (1.90%) net Self-efficacy (Liang, - 50-Upper: 1 Wu and Tsai, 2011) (0.30%) Dependent variable(s): Attitudes towards web- based continued learn- ing [68] Design: Survey Independent varia- ble(s): - The expectation– confirmation model [70] , [71] - Experiential [72] , [73] - Perceived usefulness [74] - Perceived self-efficacy [1] , [75] Dependent variable(s): Intention to continue learning [70] iJET ‒ Vol. 13, No. 9, 2018 71

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review Authors Sample Charac- Study design Finding (Year) teristics Lin, et al. [76] Design: Survey - Teaching presence significantly N: 210 Independent varia- predicted self-efficacy (! = Shen [77] Gender: ble(s): .0217, t = 2.503, p < .05) - Male: 122 (58.10%) - Teaching presence - - Social presence was a stronger Lim, Kang - Female: 88 (41.9%) Social presence predictor of self-efficacy (! = and Park [80] Age: Mediator variable(s): .477, t = 5.077, p < .001). - 21-30: 121 (57.6%) - Self-efficacy - Self-efficacy is a full mediator Liou, et al. - 30-40: 89 (30.5%) - Cognitive Presence between social presence and [88] Dependent variable(s): cognitive presence. N: 250 Training Effectiveness - Trust between member had a N: 937 Design: Survey significant positive effect on self- Country: Korea Independent varia- efficacy of knowledge-sharing ( Gender: ble(s): != 0.405, p < .001) - Male: 407 (43.44%) - Sense of community -Effect of perceptual learning (! - Female: 503 [78] = 0.433, p < .001) on self- (56.56%) - Community trust [79] efficacy of knowledge-sharing Age: - Self-efficacy [58] was significant. - 20: 10 Dependent variable(s): (1.07%) Knowledge Sharing - Learner-learner interaction (r = - 20–29: 189 0.28) and system quality (r = (20.17%) Design: Survey 0.18) was related to learner - 30–39: 264 (28.17) Independent varia- computer self-efficacy - 40-49: 301 ble(s): - Learner-learner interaction (r = (32.12%) - Learner-learner inter- 0.59) and content quality (r = - 49-Upper: 173 action and 0.45) was related to learner (18.46%) learner-instructor inter- academic self-efficacy action [81] - Learner-learner interaction had N: 394 - The quality of learning a significant effect on both Country: content [82] , [83] computer self-efficacy (! = .28, p Taiwan - The quality of online < .001) and academic self- Gender: learning systems used efficacy (! = .56, p < .001) - Male: 128 (32.48%) by participants [84] - Content quality significantly - Female: 266 - Extrinsic and Intrinsic predicted computer self-efficacy (67.52%) motivation [85] (! = .23, p < .001). Age: - Computer self-efficacy - System quality significantly - 21-24: 121 [86] effected academic self-efficacy (30.66%) - Academic self- (! = .37, p < .001). - 25-34: 273 efficacy [87] (55.47%) Dependent variable(s): - Anticipated reciprocal relation- - Class satisfaction ship (r = .506 , p < .001), Norm - Academic achieve- of reciprocity (r = .384, p < ment .001), and Anticipated extrinsic rewards (r = .456, p < .001) were Design: Survey significantly correlated with Independent varia- knowledge sharing self-efficacy ble(s): - Anticipated extrinsic rewards - The anticipated recip- had a significant and positive rocal relationship and effect on knowledge sharing self- anticipated extrinsic efficacy (# = 0.589, p < 0.001) rewards [89] - The knowledge sharing self- - Norm of reciprocity efficacy partially mediated [58] knowledge sharing behavior. Mediator variable(s): - Knowledge sharing self-efficacy [58] - Three items for knowledge sharing behavior [90] 72 http://www.i-jet.org

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review Authors Sample Charac- Study design Finding (Year) teristics Prior, et al. Dependent variable(s): [92] - Community participa- tion [91] Reychav, et al. [94] N: 150 Design: Survey - Attitude (r = .577, p < .01) and Gender: Independent varia- Digital literacy (r = .538, p < .01) Song, et al. - Male: 102 (68%) ble(s): were significantly correlated [97] - Female: 48 (32%) Attitude and Digital with self-efficacy. Age: literacy [93] - Attitude had a significant Vayre and - 21–30: 33 (22.4%) Mediator variable(s): positive effect on self-efficacy (! - 31–40: 60 (39.7%) Self-efficacy [61] = 0.556, p < 0.01) - 41–50: 40 (26.5%) Dependent variable(s): - Digital literacy appears to have - 51–60: 14 (9.3%) - Peer engagement had a significant positive effect - Learning-management on self-efficacy (! = 0.274, p < N: 1111 system interactions 0.05) Gender: - Convener interaction - Self-efficacy had a significant - Male: (52%) positive effect on peer engage- - Female: (48%) Design: quasi- ment (! = .694, p < .01), Learn- Mean age: 13.21 experiment Independ- ing-management system interac- ent variable(s): tions (! = .570, p < .01) and N: 386 - Network reciprocity convener interaction (! = .646, p Gender: - Eigenvector centrality < .01). - Male: 201 (52.1%) Mediator variable(s): - Network reciprocity had a - Female: 185 - Perceived ease of use strong and significant effect on (47.9%) and attitude toward computer self-efficacy (! = .16, p Country: US. technology [74] < .01) Age: - Computer self-efficacy - Perceived enjoyment had a Mean = 25 [95] positive effect on computer self- - Perceived enjoyment efficacy (! = .34, p < .01). [96] Dependent variable(s): Prior knowledge showed a - Attitude toward Tech- positive direct effect on self- nology Use efficacy (! = .3, p < .001) - Performance Design: quasi- experiment Independent varia- ble(s): - Prior knowledge - Self-regulation [97] Dependent variable(s): - Goal orientation [98] -Task value [99] , [100] - Self-efficacy [101] - Clinical reasoning [102] Dependent variable(s): Learning performance N: 255 Design: Survey - The sense of belonging to a iJET ‒ Vol. 13, No. 9, 2018 73

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review Authors Sample Charac- Study design Finding (Year) teristics Vonthron Independent varia- community played a significant [103] Gender: ble(s): and positive role on self-efficacy. - Male: 63 (24.7%) - Perceived social (! = 0.37, p < 0.01) Cho and Cho - Female: 192 support. - Academic self-efficacy partial- [108] (75.3%) - Sense of belonging to ly mediated between the sense of Age: a community [104] , belonging to the learning com- Hong, et al. 18-68 (Mean = [105] munity and enthusiasm [111] 31.60, SD = 10.70) Mediator variable(s): Academic Self-efficacy - Self-regulation in interaction Kim and Park N: 799 [106] between student and content, [112] Gender: Dependent variable(s): student and student and, student - Male: 247 (30.9%) Academic engagement and teacher significantly corre- - Female: 552 [107] lated with self-efficacy (r = .52, p (69.1%) Design: Survey < .001; r = .27, Independent varia- p < .001; r = .51, p < .001), N: 73 ble(s): respectively. Country: SR in the three types of - Self-regulation in interaction Taiwan online interaction (The between student and content (! = Gender: online .37, p < .001) had a positive - Male: 34 (46.6%) self-regulation ques- effect on self-efficacy for learn- - Female: 39 (53.4%) tionnaire [OSRQ]) ing. Age: Dependent variable(s): - Self-regulation in interaction Mean: 10.62 - Self-efficacy for between student and teacher in learning [109] online courses (! = .30, p < .001) N: 707 - Course satisfaction positively affected self-efficacy (Learner: 384 scale [110] for learning. , Instructor: 353) - The relationship between Gender: Design: quasi- Chinese learning intrinsic moti- Learner experiment vation and online learning self- - Male: 219 (62.04%) Independent varia- efficacy was supported with a - Female: 134 ble(s): path coefficient of .382 (t = 4.35, (37.96%) - Intrinsic motivation of p < 0.001) Instructor: Chinese learning - The test of the relationship - Male: 257 (66.93%) Mediator variable(s): between the degree of progress - Female: 127 - Online learning self- and online learning self-efficacy (33.07%) efficacy was supported by a path coeffi- - Flow experience cient of .222 (t = 2.37, p < .05) Dependent variable(s): Degree of learning - Personal innovativeness in the progress domain of ICT was also identi- Design: Survey fied as an important factor influ- Independent varia- encing computer self-efficacy (! ble(s): = .224, p < .001) for instructors. - Personal innovative- - Computer experience was ness significantly associated with - Computer experience computer self-efficacy for in- Mediator variable(s): structors (! = .223, p < .001) and - Computer self-efficacy for learners (! = .141, p < .05). - Performance expecta- tion - Computer self-efficacy partially 74 http://www.i-jet.org

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review Authors Sample Charac- Study design Finding (Year) teristics Dependent variable(s): mediated the effects of personal Age: Behavioral intention to experiences and innovativeness Learner use e-learning systems. in ICT on performance expecta- - Lower-20: 95 tion in the case of instructors, (24.74%) and it partially mediated their - 20-25: 262 effect on expectation in the case (68.23%) of learners. - 25-Upper: 27 (7.03%) Mean: 21.4 Instructors - 20-29: 171 (48.44%) - 30-39: 98 (27.76%) - 40-49: 54 (15.30%) - 50-59: 27 (7.65%) - 50-Upper: 3 (0.85%) Mean: 33.68 3.2 Factors influencing self-efficacy in online learning The focus of the research question is on the factors that influence self-efficacy in the online learning environment. Self-efficacy perceptions can and do change as a result of environmental, cognitive, and behavioural effects that a person experiences in the course of everyday life [1] , [3]. This study’s findings define specific factors that literature reported as having a perceived effect on self-efficacy in the online lear- ning environment. The result of Bates and Khasawneh [21] reported that previous online learning, instructor-acquired skill, instructor feedback, and online-learning system anxiety influenced students’ self-efficacy in the context of online learning. These factors are consistent with the sources of self-efficacy introduced by Bandura [1] which states that self-efficacy expectations are based on four major sources of information: enactive mastery experience, vicarious experience, verbal persuasion as well as physiological and affective states. Findings are described on this topic as a set of categories which follow. Online Learning Experience and Knowledge. Eight studies showed strong ag- reement on the effect of online learning experience and knowledge on self-efficacy. Choi, et al. [24] revealed that flow experience has a direct and indirect effect via atti- tude towards e-learning on technology self-efficacy in Enterprise Resource Planning training with a web-based e-learning (ERP) system usage. In a series of studies, Jas- hapara and Tai [10] , [54] demonstrated that computer experience influenced e- learning system self-efficacy. Moreover, these findings suggested that personal inno- vativeness with information technology (IT) and computer playfulness also influenced e-learning system self-efficacy. Kim and Park [112] investigated factors influencing an individual’s behavior to use e-learning through social-cognitive theory by exami- ning the adoption of e-learning by instructors and learners. The results showed that computer experience significantly effected computer self-efficacy for learners. Me- iJET ‒ Vol. 13, No. 9, 2018 75

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review anwhile, the personal innovativeness in the domain of information and communica- tions technology (ICT) and computer experience was also identified as an important factor influencing computer self-efficacy for instructors. Prior, et al. [92] suggested that attitude and digital literacy has a significant positive effect on self-efficacy. Shen, et al. [61] explored the dimensions of online learning self-efficacy. The result de- monstrated that online experience measured with the number of online courses was a significant predictor for self-efficacy to complete an online course. Song, Kalet and Plass [113] also examined the effects of medical clerkship students’ prior knowledge, self-regulation, and motivation on learning performance in complex multimedia learn- ing environments. The results showed that students with higher prior knowledge about a carotid disease case tended to report higher self-efficacy. Tang, Tang and Chiang [69] proposed an extended expectation-confirmation model (ECM) that expli- citly incorporated experiential learning, perceived self-efficacy, and perceived useful- ness to examine blog-continuance learning behavior intentions. The results demonst- rated that blog learners’ confirmation levels affected various learning beliefs, where the effect of perceived self-efficacy was the largest. Enactive mastery experiences are the most influential source of efficacy information because they provide the most authentic evidence of whether one can muster whatever it takes to succeed [1]. Contrarily, the experience of failure will result in recognition of self-efficacy, which leads to a lack of an attempt to complete tasks. Feedback and Reward. Two studies reported the positive effect on self-efficacy when feedback and reward were presented. The finding from Liou, et al. [88] indica- ted that members of the Yamol online-test community improved knowledge sharing self-efficacy if they anticipated extrinsic rewards. Wang and Wu [30] suggested that students who received more elaborate feedback significantly increased their self- efficacy. The benefits of feedback and reward are the opportunity to discover whether they achieve their goals in learning. Online Communication and Interactions. Six studies showed a strong agreement on the effect of online communication and interactions on self-efficacy. Cho and Cho [108] found that online-learner interaction with learner, content, and teacher are likely to demonstrate higher self-efficacy for learning and satisfaction with the course. Lim, et al. [80] also found the effect of learner-learner interaction on the computer and academic self-efficacy. Meanwhile, academic self-efficacy and computer self-efficacy were affected by content quality and system quality. Based on the community of in- quiry framework, Lin et al. [76] investigated the relationship among forms of presen- ce, self-efficacy, and training. The results showed that the teaching presence has a positive prediction on social presence, self-efficacy, and cognitive presence. Moreo- ver, self-efficacy is a full mediator between social presence and cognitive presence. Tseng and Kuo [40] showed influences of community identity and interpersonal trust on knowledge-sharing behaviour through the mediation of social awareness and knowledge-sharing self-efficacy. Reychav, et al. [94] investigated the effect of social network on mobile collaboration with a focus on two aspects of social network me- chanism, namely eigenvector centrality and network reciprocity. The results indicated that the network reciprocity formed through peer interactions between users in their daily lives can be leveraged when mobile devices are used in collaborative work. 76 http://www.i-jet.org

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review Shen [77] explored the impact of social interaction, perceptual learning, trust, a sense of community and self-efficacy for knowledge sharing among members in communi- ty. The empirical results showed that trust between members and perceptual learning has a significant effect to self-efficacy of knowledge sharing in virtual learning com- munity. Vayre and Vonthron [103] reported that the sense of community plays an important role regarding self-efficacy in online education. Zang, et al. [56] reported effects of two environmental-communication factors, namely, psychological safety- communication climate and perceived responsiveness on self-efficacy. The online communication and interaction not only allow learners to express themselves but also increase opportunities for learners to receive recognition of successful from each other. Online learning does not readily foster opportunities for observing peer success. Vicarious experience refers to one’s observation of a role model performing a task successfully. Verbal persuasion can lead to higher self-efficacy by encouragements from others. Therefore, self-efficacy would be reduced if the learners fail to commu- nicate and meet the performance of others. Verbal persuasion has limitations but can be powerful in conjunction with the role models of the individuals. One possibility for addressing the vicarious experience and verbal persuasion in online learning is for users to encourage communication and to share their successful experiences. Social Influence. Three studies investigated the effect of social influence on self- efficacy. Social factor is defined as an individual’s internalization of the reference group’s subjective culture, and specific interpersonal agreements that the individual has made with others, in specific social situations [114]. Chiu and Tsai [66] revealed that the facilitating factor of social contexts in the workplace is an influential way of raising nurses’ internet self-efficacy. In addition, the social factor plays an indirect role in the nurses’ intentions to use web-based continuing learning through basic internet self-efficacy. Chu and Chu [35] proposed the role of collectivism and group potency at group level in predicting individual internet self-efficacy and individual e- learning outcomes for people older than forty-five. The results showed that internet self-efficacy fully mediates the relationship between peer support and learners’ persis- tence in e-learning. In addition, collectivism also moderates the relationship between peer support and internet self-efficacy. Chu [32] further indicated that family support had a most significant role in predicting the effects of e-learning, mediated by general and communication internet self-efficacy. In the gender model, men generally relied more on emotional support to enhance their communication-internet self-efficacy, whereas women showed more reliance on tangible support to increase their communi- cation via the Internet. Social support is an important resource that can help individu- als improve self-efficacy and handle stress. The last source of information is the direct effect physiological states can have on learners’ self-efficacy. When people judge stress and anxiety, they depend on their state of physiological arousal. It is very likely that individuals will succeed if they are not in a state of aversive arousal [1]. In Chiu and Tsai [66] study, a head nurse or co-worker who is successful in utilizing online learning can serve as a role model for nursing staff. Learner Motivation and Attitude. Three studies indicated that learner motivation and attitude was the main factor affecting the self-efficacy of the online learner. Mo- tivation can be defined as the extent to which persistent effort is directed toward a iJET ‒ Vol. 13, No. 9, 2018 77

:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review goal [115]. Motivation can be determined intrinsically by individuals and externally by sources due to situational variables and environmental factors [116]. Hong, et al. [111] proposed that intrinsic motivation of Chinese learning could positively predict online learning self-efficacy. Law, et al. [39] reported that three motivating factors, namely, individual attitude and expectation, challenging goals, and social pressure and competition had a significant and positive relationship with self-efficacy. The t- test was used to compare the mean scores of constructs between male and female students. Male students are apparently more motivated by challenges, and they also showed a higher level of self-efficacy than female students. Wang, et al. [62] sugges- ted that the level of motivation directly influenced the level of technology self- efficacy. Self-efficacy and motivation have a complex interrelationship. It is likely that each influences or supports the other. However, motivation may be strong enough to overcome a weaker sense of self-efficacy. 3.3 Limitation The main limitations are the fact that only published papers written in English between 2005 and 2017 were included in the review process. Most of the selected studies applied survey design. More rigorous research design, such as incorporating a comparison group, is needed to conclude that the reported literature conclusively had an effect on self-efficacy in online learning. 4 Conclusion Self-efficacy is the key to success in all activities including online learning. Hence, the understanding of the source of self-efficacy in online learning context is im- portant. As found in this systematic review, many researchers focused on the investigation of various factors that influenced learner self-efficacy in online learning context. These various factors were source of self-efficacy in online learning context as follows: online learning experience and knowledge, feedback and reward, online communication and interactions, social influence, and learner motivation and attitude. Moreover, the results of this review can be guidance in further research for design online learning to enhance self-efficacy of learner. 5 References [1] Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman. [2] Bandura, A. (1988). Perceived self-efficacy: Exercise of control through self-belief. In J. Dauwalder, P. Perrez, M. and Hobi, V. (Eds.), Annual series of European research in be- havior therapy, 2: 27-59 [3] Schunk, D. (1991). Self-efficacy and academic motivation. Educational Psychologist, 26 (3-4): 207-231 https://doi.org/10.1080/00461520.1991.9653133 78 http://www.i-jet.org

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:Paper—An Exploration of Factors Influencing Self-Efficacy in Online Learning A Systematic Review [115] Johns, G. (1996). Organizational behaviour: Understanding and managing life at work, 4th ed., New York: Harper Collins. [116] Ryan, R. M. and Deci, E. L. (2000). Self-determination theory and the facilitation of in- trinsic motivation, social development and well-being, American Psychologist, 55: 68–78 https://doi.org/10.1037/0003-066X.55.1.68 6. Authors Chattavut Peechapol is currently a Ph.D. Student in Technopreneurship and Innovation Management Program at the Chulalongkorn University, Bangkok 10330, Thailand. He also works at the Southeast Asia University, Bangkok, Thailand. His main research interests in educational innovation and information technology management. E-mail: [email protected] Jaitip Na-Songkhla is currently an Associate Professor in the Department of Educational Technology and Communications, Chulalongkorn University, Bangkok 10330, Thailand. Her main research interests are educational technology development and educational policy studies. E-mail: [email protected] Siridej Sujiva is currently an Associate Professor in the Department of Educational Research and Psychology, Faculty of Education, Chulalongkorn University, Bangkok 10330, Thailand. His main research interests are educational measurement and evalu- ation. E-mail: [email protected] Arthorn Luangsodsai is currently a Lecturer in the Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand. His main research interests model-based slicing, program slicing, software engineering, outlier detection, information management, databases and cloud computing. E-mail: [email protected] Article submitted 04 February 2018. Resubmitted 02 March 2018. Finala cceptance 23 March 2018. Final version published as submitted by the authors. 86 http://www.i-jet.org